Instructions to use NTCAL/GPU88 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTCAL/GPU88 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NTCAL/GPU88")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NTCAL/GPU88") model = AutoModelForSequenceClassification.from_pretrained("NTCAL/GPU88", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c07513bc02997d34899e2f849d79e22dc8aef9740e6c3a3144cc023cebededee
- Size of remote file:
- 3.52 kB
- SHA256:
- f157fdc03b276fdbcce83403f65ceffda7c6b07deaf20c9fe0f15af15c191091
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